Example Curriculum
Introduction
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days
days
after you enroll
Deep Dive into LangChain
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days
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after you enroll
- Introduction to LangChain (7:15)
- Setting Up the Environment: LangChain, Python-dotenv (7:05)
- ChatModels: GPT-3.5-Turbo and GPT-4 (6:29)
- Caching LLM Responses (4:56)
- LLM Streaming (2:57)
- Prompt Templates (5:35)
- ChatPromptTemplate (5:54)
- Simple Chains (6:55)
- Sequential Chains (7:14)
- Introduction to LangChain Agents (4:00)
- LangChain Agents in Action: Python REPL (7:40)
- LangChain Tools: DuckDuckGo and Wikipedia (11:07)
- Creating a React Agent (13:29)
- Testing the React Agent (4:49)
LangChain and Vector Stores (Pinecone)
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days
days
after you enroll
- Short Recap of Embeddings (1:52)
- Introduction to Vector Databases (6:57)
- Authenticating to Pinecone (4:26)
- Working with Pinecone Indexes (9:31)
- Working with Vectors (8:42)
- Namespaces (6:43)
- Splitting and Embedding Text Using LangChain (9:19)
- Inserting the Embeddings into a Pinecone Index (8:49)
- Asking Questions (Similarity Search) (7:53)
LangChain and Google's Gemini Models
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days
days
after you enroll
Project: RAG - Q&A Application on Your Private Documents (Pinecone & Chroma)
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days
days
after you enroll
- Project Introduction (6:08)
- Loading Your Custom (Private) PDF Documents (7:27)
- Loading Different Document Formats (5:12)
- Public and Private Service Loaders (4:37)
- Chunking Strategies and Splitting the Documents (6:38)
- Embedding and Uploading to a Vector Database (Pinecone) (13:33)
- Asking and Getting Answers (10:33)
- Using Chroma as a Vector DB (11:10)
- Adding Memory to the RAG System (Chat History) (9:25)
- Using a Custom Prompt (8:09)
Where To Go From Here?
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days
days
after you enroll